Genetic Relational Search for Inductive Concept Learning

Genetic Relational Search for Inductive Concept Learning


Marketed By :  LAP LAMBERT Academic Publishing   Sold By :  Kamal Books International  
Delivery in :  10-12 Business Days

₹ 5,066

Availability: Out of stock


Delivery :

5% Cashback on all Orders paid using MobiKwik Wallet T&C

Free Krispy Kreme Voucher on all Orders paid using UltraCash Wallet T&C
Product Out of Stock Subscription

(Notify me when this product is back in stock)

  • Product Description

Learning from examples in First Order Logic, also known as Inductive Logic Programming (ILP), constitutes a central topic in Machine Learning, with relevant applications to problems in complex domains, e.g., natural language and computational biology. Learning can be viewed as a search problem in the space of all possible hypotheses. Given a background knowledge, a set of positive examples and a set of negative examples, expressed in First Order Logic, one has to find a hypothesis which covers all positive examples and none of the negative ones. This problem is NP-hard even if the language to represent hypotheses is propositional logic. When FOL hypotheses are used, this complexity is combined with the complexity of evaluating hypotheses. This book describes an evolutionary algorithm for ILP. The algorithm, called ECL (for Evolutionary Concept Learner), evolves a population of Horn clauses by repeated selection, mutation and optimization of more fit clauses. ECL relies on four greedy mutation operators for searching the hypothesis space, and employs an optimization phase that follows each mutation. Experimental results show that ECL works well in practice.

Product Specifications
SKU :COC38774
Country of ManufactureIndia
Product BrandLAP LAMBERT Academic Publishing
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-01-08
0 Review(s)